Modelling bird species richness with neural networks for forest

Abstract

For preserving biodiversity of European-Mediterranean forest ecosystems in current and future scenarios of global change by means of sustainable forest management it is necessary to determine how environment and forest characteristics correlate with biodiversity. For this purpose, neural networks were used to model forest bird species richness as a function of environment and forest structure and composition at the 1 × 1 km scale in Catalonia (NE Spain). Univariate and multivariate models respectively allowed exploring individual variable response and obtaining a parsimonious (ecologically meaningful) and accurate neural network. Forest area (with a canopy cover above 5%), mean forest canopy cover, mean annual temperature and summer precipitation were the best predictors of forest bird species richness. The resultant multivariate network had a good generalization capacity that failed however in the locations with highest species richness. Additionally, those forests with different degrees of canopy closure that were more mature and presented a more diverse tree species composition were also associated with higher bird species richness. This allowed us to provide management guidelines for forest planning in order to promote avian diversity in this European-Mediterranean region.Key words: global change; fires; forest biodiversity; forest landscape composition and structure; forest planning; landscape diversity. ResumenModelización de la riqueza de aves mediante redes neuronales para la gestión forestal a escala de paisaje en el NE de España Para preservar la biodiversidad de los ecosistemas forestales de la Europa mediterránea en escenarios actuales y futuros de cambio global mediante una gestión forestal sostenible es necesario determinar cómo influye el medio ambiente y las propias características de los bosques sobre la biodiversidad que éstos albergan. Con este propósito, se analizó la influencia de diferentes factores ambientales y de estructura y composición del bosque sobre la riqueza de aves forestales a escala 1 × 1 km en Cataluña (NE de España). Se construyeron modelos univariantes y multivariantes de redes neuronales para respectivamente explorar la respuesta individual a las variables y obtener un modelo parsimonioso (ecológicamente interpretable) y preciso. La superficie de bosque (con una fracción de cabida cubierta superior a 5%), la fracción de cabida cubierta media, la temperatura anual y la precipitación estival medias fueron los mejores predictores de la riqueza de aves forestales. La red neuronal multivariante obtenida tuvo una buena capacidad de generalización salvo en las localidades con una mayor riqueza. Además, los bosques con diferentes grados de apertura del dosel arbóreo, más maduros y más diversos en cuanto a su composición de especies arbóreas se asociaron de forma positiva con una mayor riqueza de aves forestales. Finalmente, se proporcionan directrices de gestión para la planificación forestal que permitan promover la diversidad ornítica en esta región de la Europa mediterrá...

Landscape diversity influences the arthropod species diversity in the rice field

“…This type of result can be observed in other reports. Gil-Tena et al (2010) reported that all the variables show positive signs, but aspect diversity, temperature, public forest, and road and population density, display an inverse relationship.…”

Spatial relationships between above-ground biomass and bird species biodiversity in Palawan, Philippines

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Trends in modeling to address forest management and environmental challenges in Europe: Introduction

“…The Special Issue is structured around five review articles focused on five questions that were chosen based on of their intrinsic scientific importance as well as their significance for decision-making in forest management: (i) Models for supporting forest management in a changing environment (Fontes et al, 2010); (ii) Recent approaches to model the risk of storm and fire to European forests and their integration into simulation and decision support tools (Hanewinkel et al, 2010); (iii) Simulating wood quality in forest management models (Mäkelä et al, 2010); (iv) Modelling non-wood forest products in Europe: a review (Calama et al, 2010); and lastly, (v) Simulation tools for decision support to adaptive forest management in Europe (Muys et al, 2010). In addition, the special issue is complemented with two original papers dealing with forest genetics modeling (Kramer and Van der Werf, 2010) and an example of modeling forest birds using neural networks (Gil-Tena et al, 2010).…”